Conventional fixed wing aircraft rely on an ensemble of legacy air data sensors to obtain critical air data measurands required for the flight management system, and cockpit instruments and displays. A measurand is a physical quantity or property which is measured, such as total air temperature or static pressure. Some of these sensors suffer from long-term stability problems, most notably the pitot and pitot-static probes which are susceptible to failure caused by icing or other obstructions. Although much research and development has been dedicated to overcoming the shortcomings of these sensors, the fundamental limitations still remain. Recently, Light Detection and Ranging (LiDAR, also LIDAR or LADAR) has been identified as a potential technology platform for air data acquisition that circumvents the stability problems of pitot based sensors.
LiDAR remotely interrogates a volume of free stream air. An optical beam is emitted from a laser and the backscattered optical beam is analyzed to derive air data parameters. The emitted optical beam that is backscattered from the volume of air is referred to as a backscattered reflection of the optical beam.
The optical beam can be reflected based on Mie scattering only, Rayleigh scattering only, or a combination of Mie scattering and Rayleigh scattering. As is known to one skilled in the art, the Mie solution to Maxwell's equations (also known as the Lorenz-Mie solution, the Lorenz-Mie-Debye solution, or Mie scattering) describes the (scattering of an electromagnetic plane wave by a homogeneous sphere. The solution takes the form of an infinite series of spherical multipole partial waves. As is known to one skilled in the art, Rayleigh scattering describes the elastic scattering of light by spheres that are much smaller than the wavelength of light. Rayleigh scattering has a strong wavelength dependence and increases rapidly with decreasing wavelength.
The air data parameters derived from the analysis of the backscattered reflection of the optical beam include, but are not limited to, true air speed vector, true air temperature, and static pressure. The air speed vector is derived from the Doppler shift of the backscattered return. The air temperature and air pressure are derived from the lineshape of the backscattered spectrum. Because the LiDAR optical sensor head can be flush mounted to the aircraft vehicle and fully enclosed in the interior, it circumvents the reliability and aerodynamic drag issues encountered by extruding pitot tubes and temperature sensors. Furthermore, there are no fundamental limitations of reduced accuracy at high angles of attack or low velocity. Due to these many advantages, LiDAR technology is seemingly well positioned to displace the antiquated Legacy air data technology.
FIG. 1 shows a common LiDAR backscatter lineshape 100 for Mie scattering and Rayleigh scattering. Mie scattering and Rayleigh scattering both contribute to the LiDAR backscatter lineshape 100. The LiDAR backscatter lineshape 100 includes the Mie scattered contribution (i.e., an aerosol peak 102), which protrudes from the generally bell-shaped portion of the LiDAR backscatter lineshape 100.
Airspeed, which is related to the velocity of a moving vehicle, is derived from the Doppler shift between the center frequency v1, of the LiDAR backscattered signal and the laser frequency v2. The Doppler shift is proportional to the Δv=(v1−v2) (FIG. 1). The center frequency is v1 and v2 is the frequency of the LiDAR backscattered optical beam. The laser frequency v2 is the frequency of the beam emitted by the laser in the LiDAR system.
Both the air temperature and the air pressure are convolved (convoluted) in the molecular linewidth of the LiDAR backscattered spectrum (i.e., the LiDAR backscatter lineshape 100, which is also referred to as the molecular lineshape 100). The width (W) of the molecular lineshape 100 is dictated by the air temperature. The intensity (i.e., the area under the curve) of the molecular lineshape 100 is dictated by the density, which is directly related to the air pressure. Typically, a model is used to fit and subsequently deduce the air data parameters from the molecular lineshape 100. The air temperature and air pressure are interrelated via the well known ideal gas law (PV=nRT), which makes it difficult to determine the source of any deviations in the LiDAR backscattered lineshape 100. It is difficult to deconvolve (deconvolute) the signal into accurate air data measurands. The backscattered signal is often noisy, which makes it more difficult to resolve small changes in air temperature and air pressure.
FIG. 2 shows simulations of four almost overlapping normalized LiDAR backscattered lineshapes 101-A, 101-B, 101-C, and 101-D for four respective different air pressure and temperature conditions. The overlapping normalized LiDAR backscattered lineshapes 101-A, 101-B, 101-C, and 101-D are represented in combination as 101 (FIG. 2). The lineshapes 101-A, 101-B, 101-C, and 101-D are simulated for Rayleigh scattering only.
FIG. 2i is an expanded view of a peak region 105 of the LiDAR backscattered lineshapes 101-A, 101-B, 101-C, and 101-D of FIG. 2. FIG. 2ii is an expanded view of a minimal region 106 of the LiDAR backscattered lineshapes 101-A, 101-B, 101-C, and 101-D of FIG. 2. In FIGS. 2, 2i, and 2ii, the exemplary lineshapes 101-A, 101-B, 101-C, and 101-D shown in FIGS. 2, 2i, and 2ii are ideal molecular line shapes with no noise.
The LiDAR backscattered lineshape 101-A is obtained when the measured temperature is 263.15° K and the measured pressure is 0.6 atmospheres. The LiDAR backscattered lineshape 101-B is obtained when the measured temperature is 273.15° K and the measured pressure is 0.5 atmospheres. The LiDAR backscattered lineshape 101-C is obtained when the measured temperature is 263.15° K and the measured pressure is 0.5 atmospheres. The LiDAR backscattered lineshape 101-D is obtained when the measured temperature is 273.15° K and the measured pressure is 0.6 atmospheres.
As shown in FIG. 2i, the shapes of the LiDAR backscattered lineshapes 101-A and 101-D overlap with each other but are separate from the LiDAR backscattered lineshapes 101-C and 101-B. The shapes of the LiDAR backscattered lineshapes 101-B and 101-C overlap with each other. As shown in FIG. 2ii, the shapes of the LiDAR backscattered lineshapes 101-A, 101-B, 101-C, and 101-D do not overlap. In FIG. 2ii, the LiDAR backscattered lineshapes 101-A and 101-C are closer to each other than they are to the LiDAR backscattered lineshapes 101-B and 101-D. In FIG. 2ii, the LiDAR backscattered lineshapes 101-B and 101-D are relatively close to each other.
It is clear, from FIGS. 2, 2i, and 2ii, that changes in pressure and temperature (i.e., ΔT of 10° K and/or ΔP of 0.1 atmosphere) result in subtle changes in the lineshape. The subtlety of the changes makes it difficult to extract highly accurate data parameters. As defined herein, high accuracy or accuracy is the accuracy consistent with commercial aircraft requirements. When noise, which is inherent in sensor systems, is added to the lineshape, accurate de-convolution of the lineshape is even more difficult. FIGS. 2i and 2ii clearly show why current state-of-the-art LiDAR systems have difficulty in achieving highly accurate total air temperature and static air pressure measurements for air data application. This inaccuracy limits the attractiveness of current state-of-the-art LiDAR data systems as a sole replacement for Legacy air data sensors.